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import streamlit as st
import torch
from transformers import AutoModelForSequenceClassification as ASC
from transformers import AutoTokenizer as AT
model = ASC.from_pretrained("rickxzo/albert-large-v2-s.a.m-nli")
tokenizer = AT.from_pretrained("rickxzo/albert-large-v2-s.a.m-nli")
def infer(sentence1, sentence2):
inputs = tokenizer(sentence1, sentence2, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=-1)
return torch.argmax(probs).item()
st.title("Contradiction Detector using AlBERT model")
premise = st.text_area("Enter the premise: ")
hypothesis = st.text_area("Enter the hypothesis: ")
if premise and hypothesis:
k = infer(premise, hypothesis)
if k == 2:
st.write("#### **Contradicting Statements Detected!**")
elif k == 1:
st.write("#### **Neutral Statements Detected.**")
elif k == 0:
st.write("#### **Entailing Statements Detected.**")
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